# -*- coding: utf-8 -*-
"""
 CPFNet的resnet的backbone
"""

import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torchsummary

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']

model_urls = {
	'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
	'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
	'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
	'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
	'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


def conv3x3(in_planes, out_planes, stride=1):
	"""3x3 convolution with padding"""
	return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
	                 padding=1, bias=False)


class BasicBlock(nn.Module):
	expansion = 1
	
	def __init__(self, inplanes, planes, stride=1, downsample=None):
		super(BasicBlock, self).__init__()
		self.conv1 = conv3x3(inplanes, planes, stride)
		self.bn1 = nn.BatchNorm2d(planes)
		self.relu = nn.ReLU(inplace=True)
		self.conv2 = conv3x3(planes, planes)
		self.bn2 = nn.BatchNorm2d(planes)
		self.downsample = downsample
		self.stride = stride
	
	def forward(self, x):
		residual = x
		
		out = self.conv1(x)
		out = self.bn1(out)
		out = self.relu(out)
		
		out = self.conv2(out)
		out = self.bn2(out)
		
		if self.downsample is not None:
			residual = self.downsample(x)
		
		out += residual
		out = self.relu(out)
		
		return out


class Bottleneck(nn.Module):
	expansion = 4
	
	def __init__(self, inplanes, planes, stride=1, downsample=None):
		super(Bottleneck, self).__init__()
		self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
		self.bn1 = nn.BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
		                       padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(planes)
		self.conv3 = nn.Conv2d(planes, planes * Bottleneck.expansion, kernel_size=1, bias=False)
		self.bn3 = nn.BatchNorm2d(planes * Bottleneck.expansion)
		self.relu = nn.ReLU(inplace=True)
		self.downsample = downsample
		self.stride = stride
	
	def forward(self, x):
		residual = x
		
		out = self.conv1(x)
		out = self.bn1(out)
		out = self.relu(out)
		
		out = self.conv2(out)
		out = self.bn2(out)
		out = self.relu(out)
		
		out = self.conv3(out)
		out = self.bn3(out)
		
		if self.downsample is not None:
			residual = self.downsample(x)
		
		out += residual
		out = self.relu(out)
		
		return out


class ResNet(nn.Module):
	def __init__(self, block, layers, num_classes=1000, deep_base=False, stem_width=32):
		self.inplanes = stem_width * 2 if deep_base else 64
		
		super(ResNet, self).__init__()
		if deep_base:
			self.conv1 = nn.Sequential(
				nn.Conv2d(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False),
				nn.BatchNorm2d(stem_width),
				nn.ReLU(inplace=True),
				nn.Conv2d(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False),
				nn.BatchNorm2d(stem_width),
				nn.ReLU(inplace=True),
				nn.Conv2d(stem_width, stem_width * 2, kernel_size=3, stride=1, padding=1, bias=False),
			)
		else:
			self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
			                       bias=False)
		
		self.bn1 = nn.BatchNorm2d(self.inplanes)
		self.relu = nn.ReLU(inplace=True)
		self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
		self.layer1 = self._make_layer(block, 64, layers[0])
		self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
		self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
		self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
		self.avgpool = nn.AvgPool2d(7, stride=1)
		self.fc = nn.Linear(512 * block.expansion, num_classes)
		
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
				m.weight.data.normal_(0, math.sqrt(2. / n))
			elif isinstance(m, nn.BatchNorm2d):
				m.weight.data.fill_(1)
				m.bias.data.zero_()
	
	def _make_layer(self, block, planes, blocks, stride=1, atrous=1):
		downsample = None
		if stride != 1 or self.inplanes != planes * block.expansion:
			downsample = nn.Sequential(
				nn.Conv2d(self.inplanes, planes * block.expansion,
				          kernel_size=1, stride=stride, dilation=atrous, bias=False),
				nn.BatchNorm2d(planes * block.expansion),
			)
		
		layers = []
		layers.append(block(self.inplanes, planes, stride, downsample))
		self.inplanes = planes * block.expansion
		for i in range(1, blocks):
			layers.append(block(self.inplanes, planes))
		
		return nn.Sequential(*layers)
	
	def forward(self, x):
		x = self.conv1(x)
		x = self.bn1(x)
		x = self.relu(x)
		x = self.maxpool(x)
		
		x = self.layer1(x)
		x = self.layer2(x)
		x = self.layer3(x)
		t = self.layer4(x)
		
		# x = self.avgpool(x)
		# x = x.view(x.size(0), -1)
		# x = self.fc(x)
		
		return t


def resnet18(pretrained=False, **kwargs):
	"""Constructs a ResNet-18 model.

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
	return model


def resnet34(pretrained=False, **kwargs):
	"""Constructs a ResNet-34 model.

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
	model_dict = model.state_dict()
	
	model_path = "./"
	if pretrained:
		pretrained_dict = model_zoo.load_url(model_urls['resnet34'], model_dir=model_path)  # Modify 'model_dir' according to your own path
		print('Petrain resnet34 Model Have been loaded!')
		# pretrained_dict =  {k: v for k, v in pretrained_dict.items() if k in model_dict}
		# model_dict.update(pretrained_dict)
		model.load_state_dict(pretrained_dict)
	return model


def resnet50(pretrained=False, **kwargs):
	"""Constructs a ResNet-50 model.

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
	model_path = "./"
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
		pretrained_dict = model_zoo.load_url(model_urls['resnet50'], model_dir=model_path)  # Modify 'model_dir' according to your own path
		print('Petrain resnet50 Model Have been loaded!')
		# pretrained_dict =  {k: v for k, v in pretrained_dict.items() if k in model_dict}
		# model_dict.update(pretrained_dict)
		model.load_state_dict(pretrained_dict)
	return model


def resnet101(pretrained=False, **kwargs):
	"""Constructs a ResNet-101 model.

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
	model_dict = model.state_dict()
	model_path = "./"
	
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
		pretrained_dict = model_zoo.load_url(model_urls['resnet101'], model_dir=model_path)  # Modify 'model_dir' according to your own path
		print('Petrain resnet101 Model Have been loaded!')
		# pretrained_dict =  {k: v for k, v in pretrained_dict.items() if k in model_dict}
		# model_dict.update(pretrained_dict)
		model.load_state_dict(pretrained_dict)
	return model


def resnet152(pretrained=False, **kwargs):
	"""Constructs a ResNet-152 model.

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
	if pretrained:
		model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
	return model


if __name__ == '__main__':
	import os
	
	net = resnet101(pretrained=True)
	net.cuda()
	torchsummary.summary(net, (3, 256, 256))